Comprehensive energy-saving control method for heating and ventilation equipment of an intelligent building

Abstract

The invention discloses a comprehensive energy-saving control method for heating and ventilation equipment of an intelligent building. The comprehensive energy-saving control method comprises the following steps: (1) transmitting and storing historical operation data of each heating and ventilation equipment in the intelligent building and real-time environment condition data outside the intelligent building into a basic database; (2) after the energy-saving diagnosis part obtains the data in the basic database, calculating a predicted load value of the heating and ventilation system in the intelligent building, and generating a dynamic heating and ventilation equipment operation strategy; (3) the controller obtains a heating and ventilation equipment operation strategy and controls the operation of each heating and ventilation equipment; (4) transmitting and storing the actually measured load value of the heating and ventilation equipment corresponding to the predicted load value in abasic database during operation; And (5) after the energy-saving diagnosis part obtains historical operation data in the basic database, real-time environment condition data outside the intelligent building and the actually measured building load value corresponding to the previous predicted load value, the next predicted load value of the heating and ventilation system in the intelligent building is calculated, and a dynamic heating and ventilation equipment operation strategy is generated.

Description

technical field [0001] The invention relates to the field of HVAC in intelligent buildings, in particular to a comprehensive energy-saving control method for HVAC equipment in intelligent buildings. Background technique [0002] In the process of building operation, the HVAC system is turned on throughout the year. Due to its long running time and higher annual energy consumption compared with ordinary building equipment, it is necessary to develop energy-saving controls with high energy-saving performance for the building HVAC system. Strategies to reduce energy consumption levels of building HVAC systems are of great significance. [0003] In the existing technology, the traditional building cooling station control method is mostly PID negative feedback control, and PID negative feedback control has relatively serious hysteresis, and the energy-saving control effect is not ideal. At present, the feed-forward control methods mainly include linear regression method, neural ...

AI Technical Summary

Problems solved by technology

In practical applications, the prediction accuracy of the linear regression method is poor, and the application of the neural network algorithm has many limitations.

Since the building load is difficult to predict, multi-time-varying and other characteristics are mainly affected by uncontrollable external influences, the accuracy of building load forecasting is the fundamental factor for whether the feedforward control strategy of the air conditioning system is reasonable, and the existing load forecasting control system is applied to buildings. HVAC system deficiencies are more pronounced

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